class_docstr: HuggingFace Text Generation Inference LLM
     Generates text using HuggingFace inference API.
  
  Attributes:
      max_new_tokens: Max number of tokens to generate.
      top_k: Sample from top k tokens.
      top_p: Sample from top p probability.
      typical_p: typical probability of a token.
      temperature: Controls randomness in the model. Lower values will make the model more deterministic and higher values will make the model more random.
      repetition_penalty: controls the likelihood of repeating same tokens based on value.
      truncate: truncate the input to the maximum length of the model.
      stop_sequences: A stop sequence is a string that stops the model from generating tokens.
      seed: The seed to use for random generation.
      do_sample: Whether or not to use sampling.
      timeout: adding timeout restricts huggingface from waiting indefinitely for model's response.
docstr: Get the default parameters for calling text generation inference API.
docstr_tok: ["Get","the","default","parameters","for","calling","text","generation","inference","API","."]
code: def _default_params(self) -> Dict[str, Any]:
      """Get the default parameters for calling text generation inference API."""
      return {
          "max_new_tokens": self.max_new_tokens,
          "top_k": self.top_k,
          "top_p": self.top_p,
          "typical_p": self.typical_p,
          "temperature": self.temperature,
          "repetition_penalty": self.repetition_penalty,
          "truncate": self.truncate,
          "stop_sequences": self.stop_sequences,
          "do_sample": self.do_sample,
          "seed": self.seed,
      }
  
file_path: pandas-ai/pandasai/llm/huggingface_text_gen.py
imports: from typing import Optional, Any, List, Dict
  from .base import LLM
  from ..helpers import load_dotenv
  from ..prompts.base import AbstractPrompt
label_desc: Use image node_id 2 for calling the HuggingFaceTextGen obj's underlying member method code with example usage: obj._default_params() and returns: dict